Deep learning models for effective refractive indices in silicon nitride waveguides

This article displays the method of constructing deep learning models for optical mode solving, with a minimal number of exact numerical solutions to Maxwell's equations. We select a silicon nitride channel waveguide and show how the patterns in the effective refractive indices of the fundamental waveguide modes for both polarizations of light, can be uncovered with only 4-16 learning points for the entire parameter space that can be conveniently accessed using existing photo-lithographical and CMOS fabrication techniques. We also illustrate the effect of various transfer functions and neural network layouts to the overall performance of the deep learning model.